4237739658

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github

Committed by GitHub

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<a href="https://github.com/sandialabs/pyttb/commit/<a class=hub.com/sandialabs/pyttb/commit/b3ff85ba5fc135d55c53039dd120ef13563f41da">b3ff85ba5<a href="https://github.com/sandialabs/pyttb/commit/b3ff85ba5fc135d55c53039dd120ef13563f41da">">Merge </a><a class="double-link" href="https://github.com/sandialabs/pyttb/commit/<a class="double-link" href="https://github.com/sandialabs/pyttb/commit/7b365081bfb32c95365a8d3b8e0ba0300946f31c">7b365081b</a>">7b365081b</a><a href="https://github.com/sandialabs/pyttb/commit/b3ff85ba5fc135d55c53039dd120ef13563f41da"> into e1cb81f58">e1cb81f58</a>

Pull Request Pull Request #54: Tensor docs plus Linting and Typing and Black oh my

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19.05

/pyttb/export_data.py

# Copyright 2022 National Technology & Engineering Solutions of Sandia,
# LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
# U.S. Government retains certain rights in this software.

import pyttb as ttb
from .pyttb_utils import *
import numpy as np
import os

def export_data(data, filename, fmt_data=None, fmt_weights=None):
    """
    Export tensor-related data to a file.
    """
    # open file
    fp = open(filename, 'w')

    if isinstance(data, ttb.tensor):
        print('tensor', file=fp)
        export_size(fp, data.shape)
        export_array(fp, data.data, fmt_data)

    elif isinstance(data, ttb.sptensor):
        print('sptensor', file=fp)
        export_sparse_size(fp, data)
        export_sparse_array(fp, data, fmt_data)

    elif isinstance(data, ttb.ktensor):
        print('ktensor', file=fp)
        export_size(fp, data.shape)
        export_rank(fp, data)
        export_weights(fp, data, fmt_weights)
        for n in range(data.ndims):
            print('matrix', file=fp)
            export_size(fp, data.factor_matrices[n].shape)
            export_factor(fp, data.factor_matrices[n], fmt_data)
        """
        fprintf(fid, 'ktensor\n');
        export_size(fid, size(A));
        export_rank(fid, A);
        export_lambda(fid, A.lambda, fmt_lambda);   
        for n = 1:length(size(A))
            fprintf(fid, 'matrix\n');
            export_size(fid, size(A.U{n}));
            export_factor(fid, A.U{n}, fmt_data);
        end
        """

    elif isinstance(data, np.ndarray):
        print('matrix', file=fp)
        export_size(fp, data.shape)
        export_array(fp, data, fmt_data)

    else:
        assert False, 'Invalid data type for export'

def export_size(fp, shape):
    # Export the size of something to a file
    print(f'{len(shape)}', file=fp)   # # of dimensions on one line
    shape_str = ' '.join([str(d) for d in shape])
    print(f'{shape_str}', file=fp)    # size of each dimensions on the next line

def export_rank(fp, data):
    # Export the rank of a ktensor to a file
    print(f'{len(data.weights)}', file=fp) # ktensor rank on one line

def export_weights(fp, data, fmt_weights):
    # Export dense data that supports numel and linear indexing
    if not fmt_weights: fmt_weights = '%.16e'
    data.weights.tofile(fp, sep=' ', format=fmt_weights)
    print(file=fp)

def export_array(fp, data, fmt_data):
    # Export dense data that supports numel and linear indexing
    if not fmt_data: fmt_data = '%.16e'
    data.tofile(fp, sep='\n', format=fmt_data)
    print(file=fp)

def export_factor(fp, data, fmt_data):
    # Export dense data that supports numel and linear indexing
    if not fmt_data: fmt_data = '%.16e'
    for i in range(data.shape[0]):
        row = data[i,:]
        row.tofile(fp, sep=' ', format=fmt_data)
        print(file=fp)

def export_sparse_size(fp, A):
    # Export the size of something to a file
    print(f'{len(A.shape)}', file=fp)   # # of dimensions on one line
    shape_str = ' '.join([str(d) for d in A.shape])
    print(f'{shape_str}', file=fp)    # size of each dimensions on the next line
    print(f'{A.nnz}', file=fp)   # number of nonzeros

def export_sparse_array(fp, A, fmt_data):
    # Export sparse array data in coordinate format
    if not fmt_data: fmt_data = '%.16e'
    # TODO: looping through all values may take a long time, can this be more efficient?
    for i in range(A.nnz):
        # 0-based indexing in package, 1-based indexing in file
        subs = A.subs[i,:] + 1
        subs.tofile(fp, sep=' ', format="%d")
        print(end=' ', file=fp)
        val = A.vals[i][0]
        val.tofile(fp, sep=' ', format=fmt_data)
        print(file=fp)

1	# Copyright 2022 National Technology & Engineering Solutions of Sandia,
2	# LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
3	# U.S. Government retains certain rights in this software.
4
5	import pyttb as ttb	3✔
6	from .pyttb_utils import *	3✔
7	import numpy as np	3✔
8	import os	3✔
9
10	def export_data(data, filename, fmt_data=None, fmt_weights=None):	3✔
11	"""
12	Export tensor-related data to a file.
13	"""
14	# open file
15	fp = open(filename, 'w')	×
16
17	if isinstance(data, ttb.tensor):	×
18	print('tensor', file=fp)	×
19	export_size(fp, data.shape)	×
20	export_array(fp, data.data, fmt_data)	×
21
22	elif isinstance(data, ttb.sptensor):	×
23	print('sptensor', file=fp)	×
24	export_sparse_size(fp, data)	×
25	export_sparse_array(fp, data, fmt_data)	×
26
27	elif isinstance(data, ttb.ktensor):	×
28	print('ktensor', file=fp)	×
29	export_size(fp, data.shape)	×
30	export_rank(fp, data)	×
31	export_weights(fp, data, fmt_weights)	×
32	for n in range(data.ndims):	×
33	print('matrix', file=fp)	×
34	export_size(fp, data.factor_matrices[n].shape)	×
35	export_factor(fp, data.factor_matrices[n], fmt_data)	×
36	"""	×
37	fprintf(fid, 'ktensor\n');
38	export_size(fid, size(A));
39	export_rank(fid, A);
40	export_lambda(fid, A.lambda, fmt_lambda);
41	for n = 1:length(size(A))
42	fprintf(fid, 'matrix\n');
43	export_size(fid, size(A.U{n}));
44	export_factor(fid, A.U{n}, fmt_data);
45	end
46	"""
47
48	elif isinstance(data, np.ndarray):	×
49	print('matrix', file=fp)	×
50	export_size(fp, data.shape)	×
51	export_array(fp, data, fmt_data)	×
52
53	else:
54	assert False, 'Invalid data type for export'	×
55
56	def export_size(fp, shape):	3✔
57	# Export the size of something to a file
58	print(f'{len(shape)}', file=fp) # # of dimensions on one line	×
59	shape_str = ' '.join([str(d) for d in shape])	×
60	print(f'{shape_str}', file=fp) # size of each dimensions on the next line	×
61
62	def export_rank(fp, data):	3✔
63	# Export the rank of a ktensor to a file
64	print(f'{len(data.weights)}', file=fp) # ktensor rank on one line	×
65
66	def export_weights(fp, data, fmt_weights):	3✔
67	# Export dense data that supports numel and linear indexing
68	if not fmt_weights: fmt_weights = '%.16e'	×
69	data.weights.tofile(fp, sep=' ', format=fmt_weights)	×
70	print(file=fp)	×
71
72	def export_array(fp, data, fmt_data):	3✔
73	# Export dense data that supports numel and linear indexing
74	if not fmt_data: fmt_data = '%.16e'	×
75	data.tofile(fp, sep='\n', format=fmt_data)	×
76	print(file=fp)	×
77
78	def export_factor(fp, data, fmt_data):	3✔
79	# Export dense data that supports numel and linear indexing
80	if not fmt_data: fmt_data = '%.16e'	×
81	for i in range(data.shape[0]):	×
82	row = data[i,:]	×
83	row.tofile(fp, sep=' ', format=fmt_data)	×
84	print(file=fp)	×
85
86	def export_sparse_size(fp, A):	3✔
87	# Export the size of something to a file
88	print(f'{len(A.shape)}', file=fp) # # of dimensions on one line	×
89	shape_str = ' '.join([str(d) for d in A.shape])	×
90	print(f'{shape_str}', file=fp) # size of each dimensions on the next line	×
91	print(f'{A.nnz}', file=fp) # number of nonzeros	×
92
93	def export_sparse_array(fp, A, fmt_data):	3✔
94	# Export sparse array data in coordinate format
95	if not fmt_data: fmt_data = '%.16e'	×
96	# TODO: looping through all values may take a long time, can this be more efficient?
97	for i in range(A.nnz):	×
98	# 0-based indexing in package, 1-based indexing in file
99	subs = A.subs[i,:] + 1	×
100	subs.tofile(fp, sep=' ', format="%d")	×
101	print(end=' ', file=fp)	×
102	val = A.vals[i][0]	×
103	val.tofile(fp, sep=' ', format=fmt_data)	×
104	print(file=fp)	×

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